Netflix Exploratory Data Analysis¶

Importing Modules¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

Loading the Dataset¶

In [2]:
Netflix = pd.read_csv('netflix.csv')
Netflix.head()
Out[2]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 s1 Movie Dick Johnson Is Dead Kirsten Johnson NaN United States September 25, 2021 2020 PG-13 90 min Documentaries As her father nears the end of his life, filmm...
1 s2 TV Show Blood & Water NaN Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, TV Dramas, TV Mysteries After crossing paths at a party, a Cape Town t...
2 s3 TV Show Ganglands Julien Leclercq Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN September 24, 2021 2021 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Act... To protect his family from a powerful drug lor...
3 s4 TV Show Jailbirds New Orleans NaN NaN NaN September 24, 2021 2021 TV-MA 1 Season Docuseries, Reality TV Feuds, flirtations and toilet talk go down amo...
4 s5 TV Show Kota Factory NaN Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, Romantic TV Shows, TV ... In a city of coaching centers known to train I...
In [3]:
Netflix.shape
Out[3]:
(8807, 12)
In [4]:
Netflix.size
Out[4]:
105684
In [5]:
Netflix.count()
Out[5]:
show_id         8807
type            8807
title           8807
director        6173
cast            7982
country         7976
date_added      8797
release_year    8807
rating          8803
duration        8804
listed_in       8807
description     8807
dtype: int64

Analysis of Movies vs TV Shows¶

In [6]:
sns.set(style="darkgrid")
plt.figure(figsize=(18, 10), dpi=100)
sns.countplot(x="type", data=Netflix, palette=sns.color_palette("RdYlBu", 12))
plt.title("Count of Movies v/s TV Shows", fontsize=25, fontweight="bold", position=(0.5, 1), pad=20)
plt.xlabel("Type", fontsize=18, fontweight="bold", labelpad=15)
plt.ylabel("Count", fontsize=18, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()

Movie Ratings Analysis¶

In [7]:
netflix_movies = Netflix[Netflix["type"] == "Movie"]
netflix_shows = Netflix[Netflix["type"] == "TV Show"]
In [8]:
netflix_movies = Netflix[Netflix["type"] == "Movie"]
sns.set(style="darkgrid")
plt.figure(figsize=(18, 10), dpi=100)
sns.countplot(x="rating", data=netflix_movies, palette="Set2", order=netflix_movies['rating'].value_counts(ascending=False).index)
plt.title("Types of Ratings in Movies", fontsize=25, fontweight="bold", position=(0.5, 1), pad=20)
plt.xlabel("Rating", fontsize=18, fontweight="bold", labelpad=15)
plt.ylabel("Count", fontsize=18, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.show()

Netflix Contents Update¶

In [9]:
netflix_shows = Netflix[Netflix["type"] == "TV Show"]
netflix_date = netflix_shows[['date_added']].dropna()
netflix_date['year'] = netflix_date['date_added'].apply(lambda x : x.split(', ')[-1])
# string = "   Septemper 25, 2002"
# print(string.lstrip())
# Returns Septemper 25, 2002 without spaces in left.
netflix_date['month'] = netflix_date['date_added'].apply(lambda x : x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
In [10]:
df = netflix_date.groupby('year')
df
Out[10]:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000029086ED7B80>
In [11]:
df = netflix_date.groupby('year')['month']
df
Out[11]:
<pandas.core.groupby.generic.SeriesGroupBy object at 0x0000029086ED8FD0>
In [12]:
df = netflix_date.groupby('year')['month'].value_counts()
df
Out[12]:
year  month    
2008  February      1
2013  October       2
      August        1
      March         1
      September     1
                   ..
2021  April        53
      February     44
      May          38
      March        37
      January      36
Name: month, Length: 88, dtype: int64
In [13]:
df = netflix_date.groupby('year')['month'].value_counts().unstack()
df
Out[13]:
month April August December February January July June March May November October September
year
2008 NaN NaN NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN
2013 NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN NaN 2.0 1.0
2014 1.0 NaN 1.0 1.0 NaN NaN NaN NaN NaN 2.0 NaN NaN
2015 4.0 NaN 7.0 1.0 NaN 2.0 2.0 2.0 1.0 2.0 4.0 1.0
2016 8.0 11.0 44.0 6.0 28.0 9.0 7.0 3.0 4.0 18.0 19.0 19.0
2017 27.0 38.0 38.0 17.0 14.0 34.0 29.0 38.0 23.0 30.0 29.0 32.0
2018 28.0 34.0 61.0 24.0 24.0 27.0 28.0 35.0 27.0 36.0 45.0 43.0
2019 43.0 44.0 47.0 45.0 37.0 59.0 46.0 53.0 48.0 68.0 65.0 37.0
2020 50.0 47.0 68.0 42.0 53.0 43.0 41.0 44.0 52.0 51.0 51.0 53.0
2021 53.0 61.0 NaN 44.0 36.0 88.0 83.0 37.0 38.0 NaN NaN 65.0
In [14]:
df = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
df
Out[14]:
year 2008 2013 2014 2015 2016 2017 2018 2019 2020 2021
month
December 0.0 0.0 1.0 7.0 44.0 38.0 61.0 47.0 68.0 0.0
November 0.0 0.0 2.0 2.0 18.0 30.0 36.0 68.0 51.0 0.0
October 0.0 2.0 0.0 4.0 19.0 29.0 45.0 65.0 51.0 0.0
September 0.0 1.0 0.0 1.0 19.0 32.0 43.0 37.0 53.0 65.0
August 0.0 1.0 0.0 0.0 11.0 38.0 34.0 44.0 47.0 61.0
July 0.0 0.0 0.0 2.0 9.0 34.0 27.0 59.0 43.0 88.0
June 0.0 0.0 0.0 2.0 7.0 29.0 28.0 46.0 41.0 83.0
May 0.0 0.0 0.0 1.0 4.0 23.0 27.0 48.0 52.0 38.0
April 0.0 0.0 1.0 4.0 8.0 27.0 28.0 43.0 50.0 53.0
March 0.0 1.0 0.0 2.0 3.0 38.0 35.0 53.0 44.0 37.0
February 1.0 0.0 1.0 1.0 6.0 17.0 24.0 45.0 42.0 44.0
January 0.0 0.0 0.0 0.0 28.0 14.0 24.0 37.0 53.0 36.0
In [16]:
netflix_shows = Netflix[Netflix["type"] == "TV Show"]
netflix_date = netflix_shows[['date_added']].dropna()
netflix_date['year'] = netflix_date['date_added'].apply(lambda x : x.split(', ')[-1])

# string = "   Septemper 25, 2002"
# print(string.lstrip())
# Returns "Septemper 25, 2002" without spaces in left.

netflix_date['month'] = netflix_date['date_added'].apply(lambda x : x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]

df = netflix_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T

plt.figure(figsize=(18, 10), dpi=100)
plt.title("TV Show Updates Per Month", fontsize=25, fontweight="bold", pad=20)
plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns, fontsize=15)
plt.yticks(np.arange(0.5, len(df.index), 1), df.index, fontsize=15)

# Heatmap
plt.pcolor(df, cmap='afmhot_r', edgecolor='white', linewidths=3)

# Color Bar on left side
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=15)
cbar.ax.minorticks_on()

# Incase MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() occurs
plt.rcParams['axes.grid'] = False
plt.show()

Year-wise Analysis¶

In [17]:
netflix_movies = Netflix[Netflix["type"] == "Movie"]
plt.figure(figsize=(12,10), dpi=100)
sns.set(style="darkgrid")
sns.countplot(y="release_year", data=netflix_movies, palette="Set2", order=netflix_movies['release_year'].value_counts(ascending=False).index[0:15])
plt.title("Year-wise Movie Count", fontsize=25, fontweight="bold", position=(0.5, 1), pad=20)
plt.xlabel("Count", fontsize=15, fontweight="bold", labelpad=15)
plt.ylabel("Release Year", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=13)
plt.yticks(fontsize=13)
plt.show()

Top 10 Movies Creating Countries¶

In [18]:
# Dictionary with three keys
# Dictionary1 = {'A': 'Geeks', 'B': 'For', 'C': 'Geeks'}
# Printing keys of dictionary
# print(Dictionary1.keys())
# dict_values(['Geeks', 'For', 'Geeks'])

countries = {}
netflix_movies['country'] = netflix_movies['country'].fillna('Unknown')
cou = list(netflix_movies['country'])
for i in cou:
    # print(i)
    i = list(i.split(','))
    if len(i) == 1:
        # print(i)
        if i in list(countries.keys()):
            countries[i] += 1
        else:
            countries[i[0]] = 1
    else:
        for j in i:
            if j in list(countries.keys()):
                countries[j] += 1
            else:
                countries[j] = 1
                
# print(countries)
# {'United States': 1, 'Unknown': 1, ' Burkina Faso': 1, ' United Kingdom': 152, ' Germany': 104}

C:\Users\user\AppData\Local\Temp\ipykernel_19216\1116861018.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  netflix_movies['country'] = netflix_movies['country'].fillna('Unknown')
In [19]:
countries_fin = {}
for country, num in countries.items():
    # country = country.replace(' ','')
    if country in list(countries_fin.keys()):
        countries_fin[country] += num
    else:
        countries_fin[country] = num
        
# print(countries_fin)
countries_fin = {key: val for key, val in sorted(countries_fin.items(), key=lambda item: item[1], reverse=True)}
# print(countries_fin)
In [20]:
plt.figure(figsize=(18, 10), dpi=100)
plt.title("Top 10 Movies Creating Countries", fontsize=25, fontweight="bold", position=(0.5, 1), pad=20)
plt.ylabel("Count", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)

ax = sns.barplot(x=list(countries_fin.keys())[0:10], y=list(countries_fin.values())[0:10])
ax.set_xticklabels(list(countries_fin.keys())[0:10], rotation=20)
plt.show()

Top 10 TV Shows Creating Countries¶

In [21]:
netflix_shows['country'].head()
Out[21]:
1    South Africa
2             NaN
3             NaN
4           India
5             NaN
Name: country, dtype: object
In [22]:
countries1 = {}
netflix_shows['country'] = netflix_shows['country'].fillna('Unknown')
cou1 = list(netflix_shows['country'])
for i in cou1:
    # print(i)
    i = list(i.split(','))
    if len(i) == 1:
        # print(i)
        if i in list(countries1.keys()):
            countries1[i] += 1
        else:
            countries1[i[0]] = 1
    else:
        for j in i:
            if j in list(countries1.keys()):
                countries1[j] += 1
            else:
                countries1[j] = 1

C:\Users\user\AppData\Local\Temp\ipykernel_19216\2003169841.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  netflix_shows['country'] = netflix_shows['country'].fillna('Unknown')
In [23]:
countries_fin1 = {}
for country, num in countries1.items():
    # country = country.replace(' ','')
    if country in list(countries_fin1.keys()):
        countries_fin1[country] += num
    else:
        countries_fin1[country] = num
        
# print(countries_fin)
countries_fin1 = {key: val for key, val in sorted(countries_fin1.items(), key=lambda item: item[1], reverse=True)}
# print(countries_fin)
In [24]:
plt.figure(figsize=(18, 10), dpi=100)
plt.title("Top 10 TV Shows Creating Countries", fontsize=25, fontweight="bold", position=(0.5, 1), pad=20)
plt.xlabel("Count", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)

sns.barplot(y=list(countries_fin1.keys())[0:10], x=list(countries_fin1.values())[0:10])
plt.show()

Analysis of Duration of Movies¶

In [25]:
netflix_movies = Netflix[Netflix["type"] == "Movie"]

# To avoid SettingWithCopyWarning
pd.options.mode.chained_assignment = None

netflix_movies['duration'] = netflix_movies['duration'].str.replace(' min', '')
netflix_movies['duration'] = netflix_movies['duration'].astype(float)
# print(netflix_movies['duration'])
In [26]:
plt.figure(figsize=(10, 7), dpi=100)
plt.title("Common Duration of Movies", fontsize=25, fontweight="bold", pad=20)
plt.xlabel("Duration", fontsize=15, fontweight="bold", labelpad=15)
plt.ylabel("Density", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)

sns.set(style="darkgrid")
sns.kdeplot(data=netflix_movies['duration'], shade=True, color="b")
plt.show()

WordCloud for Genres in Movies & TV Shows¶

In [27]:
from collections import Counter

# a = ["a", "a", "b", "c", "c"]
# b = Counter(a)
# print(b) returns Counter({'a': 2, 'c': 2, 'b': 1})

genres = list(netflix_movies['listed_in'])
gen = []
for i in genres:
    i = list(i.split(','))
    for j in i:
        j = j.replace("'", '')
        gen.append(j.replace(' ',""))
g = Counter(gen)
g
Out[27]:
Counter({'Documentaries': 869,
         'Children&FamilyMovies': 641,
         'Dramas': 2427,
         'IndependentMovies': 756,
         'InternationalMovies': 2752,
         'Comedies': 1674,
         'Thrillers': 577,
         'RomanticMovies': 616,
         'Music&Musicals': 375,
         'HorrorMovies': 357,
         'Sci-Fi&Fantasy': 243,
         'Action&Adventure': 859,
         'ClassicMovies': 116,
         'AnimeFeatures': 71,
         'SportsMovies': 219,
         'CultMovies': 71,
         'Faith&Spirituality': 65,
         'LGBTQMovies': 102,
         'Stand-UpComedy': 343,
         'Movies': 57})
In [28]:
from wordcloud import WordCloud
from PIL import Image

text = list(set(gen))

plt.figure(figsize=(18, 10), dpi=100)
plt.title("WordCloud of Movie Genres", fontsize=40, fontweight="bold", pad=35)


#assigning shape to the word cloud
mask = np.array(Image.open('logo.png'))
wordcloud = WordCloud(max_words=1000,background_color="white", mask=mask, colormap='Set2', max_font_size=150).generate(str(text).replace("'", ''))

plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()
In [29]:
from collections import Counter

# a = ["a", "a", "b", "c", "c"]
# b = Counter(a)
# print(b) returns Counter({'a': 2, 'c': 2, 'b': 1})

genres = list(netflix_shows['listed_in'])
gen = []
for i in genres:
    i = list(i.split(','))
    for j in i:
        j = j.replace("'", '')
        gen.append(j.replace(' ',""))
g = Counter(gen)
In [30]:
from wordcloud import WordCloud
from PIL import Image

text = list(set(gen))

plt.figure(figsize=(18, 10), dpi=100)
plt.title("WordCloud of TV Show Genres", fontsize=40, fontweight="bold", pad=35)


#assigning shape to the word cloud
mask = np.array(Image.open('logo.png'))
wordcloud = WordCloud(max_words=1000,background_color="white", mask=mask, colormap='Set2', max_font_size=150).generate(str(text).replace("'", ''))

plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()

Lollipop Plot of Genres vs Count of Genres¶

In [31]:
g = {k: v for k, v in sorted(g.items(), key=lambda item: item[1], reverse= True)}


plt.figure(figsize=(18, 10), dpi=100)
plt.title("Count Per Genres", fontsize=25, fontweight="bold", pad=20)
plt.ylabel("Count", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=13, rotation=90)
plt.yticks(fontsize=15)


x = list(g.keys())
y = list(g.values())
plt.vlines(x, ymin=0, ymax=y, color='red')
plt.plot(x, y, "o", color='black')
plt.show()

TV Shows With Largest Number of Seasons¶

In [32]:
netflix_shows = Netflix[Netflix["type"] == "TV Show"]
features = ['title', 'duration']
durations = netflix_shows[features]

durations['no_of_seasons'] = durations['duration'].str.replace(' Season', '')
durations.head()
Out[32]:
title duration no_of_seasons
1 Blood & Water 2 Seasons 2s
2 Ganglands 1 Season 1
3 Jailbirds New Orleans 1 Season 1
4 Kota Factory 2 Seasons 2s
5 Midnight Mass 1 Season 1
In [33]:
durations['no_of_seasons']=durations['no_of_seasons'].astype(str)
durations['no_of_seasons']=durations['no_of_seasons'].str.replace('s','')
durations['no_of_seasons']=durations['no_of_seasons'].astype(int)
durations.head()
Out[33]:
title duration no_of_seasons
1 Blood & Water 2 Seasons 2
2 Ganglands 1 Season 1
3 Jailbirds New Orleans 1 Season 1
4 Kota Factory 2 Seasons 2
5 Midnight Mass 1 Season 1
In [34]:
t = ['title', 'no_of_seasons']
top = durations[t]
top = top.sort_values(by='no_of_seasons', ascending=False)
In [35]:
plt.figure(figsize=(18, 10), dpi=100)
plt.title("TV Shows with Largest Number of Seasons", fontsize=25, fontweight="bold", pad=20)
plt.xlabel("No of Seasons", fontsize=15, fontweight="bold", labelpad=15)
plt.xticks(fontsize=15, rotation=0)
plt.yticks(fontsize=13)

top20 = top[0:20]
sns.barplot(y=list(top20['title']), x=list(top20['no_of_seasons']))
#  color=(0.2, 0.4, 0.6, 0.6) R, G, B & Transparency as 4 tuple
plt.show()

Oldest US TV Shows on Netflix¶

In [36]:
us_series_data = netflix_shows[netflix_shows['country']=='United States']
oldest_us_series=us_series_data.sort_values(by='release_year')[0:20]
In [37]:
import plotly.graph_objects as go

fig = go.Figure(data=[go.Table(header=dict(values=['Title', 'Release Year'],
                                           fill_color='#B5EAEA',
                                           align='center',
                                           line_color='white',
                                           font_size=15,
                                           font=dict(color='black', size=15),
                                           height=25),
                               cells=dict(values=[oldest_us_series['title'],oldest_us_series['release_year']],
                                          fill_color='#FFBCBC',
                                          align="center",
                                          height=23))])

fig.update_layout(width=1000, height=700)
fig.show()

Latest US TV Shows on Netflix¶

In [38]:
us_series_data = netflix_shows[netflix_shows['country']=='United States']
oldest_us_series=us_series_data.sort_values(by='release_year', ascending=False)[0:20]
In [39]:
import plotly.graph_objects as go

fig = go.Figure(data=[go.Table(header=dict(values=['Title', 'Release Year'],
                                           fill_color='#B5EAEA',
                                           align='center',
                                           line_color='white',
                                           font_size=15,
                                           font=dict(color='black', size=15),
                                           height=25),
                               cells=dict(values=[oldest_us_series['title'],oldest_us_series['release_year']],
                                          fill_color='#FFBCBC',
                                          align="center",
                                          height=23))])

fig.update_layout(width=1000, height=700)
fig.show()

Content in France¶

In [40]:
netflix_fr = Netflix[Netflix['country'] == 'France']
netflix_fr = netflix_fr.dropna()
In [41]:
import plotly.express as px

fig = px.treemap(netflix_fr,
                 path=['country', 'director'],
                 color='director',
                 hover_data=['director', 'title'],
                 color_continuous_scale='lightblue')

fig.update_layout(margin=dict(t=25, l=25, r=25, b=25),
                  width=1200,
                  height=600,
                  treemapcolorway=["pink", "lightgray"])
fig.show()

C:\Users\user\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\user\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

Analysing IMDB Ratings to Get Top Rated Movies¶

In [42]:
imdb_ratings = pd.read_csv('IMDB_ratings.csv', usecols=['weighted_average_vote'])
imdb_titles = pd.read_csv('IMDB_movies.csv', usecols=['title','year','genre'])
ratings = pd.DataFrame({'Title':imdb_titles.title,
                    'Release Year':imdb_titles.year,
                    'Rating': imdb_ratings.weighted_average_vote,
                    'Genre':imdb_titles.genre})

ratings.drop_duplicates(subset=['Title','Release Year','Rating'], inplace=True)
ratings.shape

C:\Users\user\AppData\Local\Temp\ipykernel_19216\2233518489.py:2: DtypeWarning:

Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.
Out[42]:
(85852, 4)
In [43]:
ratings.dropna()
joint_data = ratings.merge(Netflix, left_on='Title', right_on='title', how='inner')
joint_data = joint_data.sort_values(by='Rating', ascending=False)
joint_data.head()
Out[43]:
Title Release Year Rating Genre show_id type title director cast country date_added release_year rating duration listed_in description
1105 Innocent 2017 9.0 Comedy s4175 TV Show Innocent Seren Yüce Ali Atay, Haluk Bilginer, Nur Sürer, Okan Yala... Turkey January 23, 2019 2017 TV-MA 1 Season Crime TV Shows, International TV Shows, TV Dramas In a peaceful, rustic town, a retired officer ...
1116 Breakout 2019 9.0 Adventure, Comedy, Drama s5394 TV Show Breakout NaN Jeanette Aw, Elvin Ng, Zhou Ying, Christopher ... NaN July 1, 2017 2010 TV-14 1 Season International TV Shows, TV Dramas After losing her family in a suspicious car cr...
441 Pulp Fiction 1994 8.9 Crime, Drama s7803 Movie Pulp Fiction Quentin Tarantino John Travolta, Samuel L. Jackson, Uma Thurman,... United States January 1, 2019 1994 R 154 min Classic Movies, Cult Movies, Dramas This stylized crime caper weaves together stor...
421 Schindler's List 1993 8.9 Biography, Drama, History s7958 Movie Schindler's List Steven Spielberg Liam Neeson, Ben Kingsley, Ralph Fiennes, Caro... United States April 1, 2018 1993 R 195 min Classic Movies, Dramas Oskar Schindler becomes an unlikely humanitari...
1442 Inception 2010 8.8 Action, Adventure, Sci-Fi s341 Movie Inception Christopher Nolan Leonardo DiCaprio, Joseph Gordon-Levitt, Ellio... United States, United Kingdom August 1, 2021 2010 PG-13 148 min Action & Adventure, Sci-Fi & Fantasy, Thrillers A troubled thief who extracts secrets from peo...
In [44]:
import plotly.express as px
top_rated = joint_data[0:10]

fig =px.sunburst(
    top_rated,
    path=['title','country'],
    values='Rating',
    color='Rating')


fig.update_layout(margin=dict(t=25, l=25, r=25, b=25),
                  width=1200,
                  height=700,
                  treemapcolorway=["pink", "lightgray"])

fig.show()

C:\Users\user\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

C:\Users\user\anaconda3\lib\site-packages\plotly\express\_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
In [45]:
country_count = joint_data['country'].value_counts().sort_values(ascending=False)
country_count = pd.DataFrame(country_count)
topcountries = country_count[0:10]
topcountries
Out[45]:
country
United States 865
India 718
United Kingdom 119
Canada 57
Philippines 50
Spain 41
Indonesia 38
South Korea 38
France 37
Nigeria 31
In [46]:
country_names = []
for each in topcountries.index:
    country_names.append(each)
    # print(each[0])
country_names
Out[46]:
['United States',
 'India',
 'United Kingdom',
 'Canada',
 'Philippines',
 'Spain',
 'Indonesia',
 'South Korea',
 'France',
 'Nigeria']
In [47]:
country_count = []
for each in topcountries.values:
    country_count.append(each[0])
    # print(each[0])
country_count
Out[47]:
[865, 718, 119, 57, 50, 41, 38, 38, 37, 31]
In [48]:
import plotly.express as px

data = dict(number=country_count, country=country_names)


fig = px.funnel(data, x='number', y='country')
fig.update_layout(margin=dict(t=50, l=50, r=50, b=50),
                  width=1200,
                  height=500)

fig.show()

Netflix TV Shows & Movies Adapted from Books¶

In [49]:
books = pd.read_csv('books.csv')
books['original_title'] = books['original_title'].str.lower()
x = Netflix
x['title'] = x['title'].str.lower()
combined = x.merge(books, left_on='title', right_on='original_title', how="inner")
In [50]:
combined['title_x'].notna().count()
Out[50]:
339
In [51]:
Netflix['title'].notna().count()
Out[51]:
8807
In [52]:
import plotly.graph_objects as go

labels = ['TV Shows from Books','TV Shows not from Books']
values = [339, 8807]

fig = go.Figure(data=[go.Pie(labels=labels, values=values)])

colors = ['mediumturquoise', 'gold', ]
fig.update_traces(
    hoverinfo='label+percent', textinfo=('value'), 
    textfont_size=20, 
    rotation=90,
    marker=dict(colors=colors, line=dict(color='#000000', width=3)))


fig.update_layout(margin=dict(t=50, l=50, r=50, b=50),
                  width=650,
                  height=500)
fig.show()

Recommendation System (Content Based)¶

In [53]:
# from sklearn.feature_extraction.text import TfidfVectorizer
# corpus = [
#     'This is the first document.', 
#     'This document is the second document.', 
#     'And this is the third one.', 
#     'Is this the first document?']

# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
In [54]:
# print(vectorizer.get_feature_names_out())
# ['and' 'document' 'first' 'is' 'one' 'second' 'the' 'third' 'this']
In [55]:
# print(X.shape)
# (4, 9)
In [56]:
# from sklearn.feature_extraction.text import TfidfVectorizer
# corpus = [
#     'This is the first document.', 
#     'This document is the second document.', 
#     'And this is the third one.', 
#     'Is this the first document?']

# # stop_words='english'
# vectorizer = TfidfVectorizer(stop_words='english')

# X = vectorizer.fit_transform(corpus)

# print(vectorizer.get_feature_names_out())
# ['document' 'second']
In [57]:
# from sklearn.feature_extraction.text import TfidfVectorizer
# corpus = [
#     'This is the first document.', 
#     'This document is the second document.', 
#     'And this is the third one.', 
#     'Is this the first document?']

# vectorizer = TfidfVectorizer(min_df=0.4)
# X = vectorizer.fit_transform(corpus)

# print(vectorizer.get_feature_names_out())
# ['document' 'first' 'is' 'the' 'this']
In [58]:
from sklearn.feature_extraction.text import TfidfVectorizer

#removing stopwords
vectorizer = TfidfVectorizer(stop_words='english')

#Replace NaN with an empty string
Netflix['description'] = Netflix['description'].fillna('')

#Construct the required TF-IDF matrix by fitting and transforming the data
tfidf_matrix = vectorizer.fit_transform(Netflix['description'])

# print(vectorizer.get_feature_names())

#Output the shape of tfidf_matrix
# print(tfidf_matrix)

tfidf_matrix.shape
Out[58]:
(8807, 18895)
In [59]:
# Import linear_kernel
from sklearn.metrics.pairwise import linear_kernel

# Compute the cosine similarity matrix
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
cosine_sim
Out[59]:
array([[1.        , 0.        , 0.        , ..., 0.        , 0.01538292,
        0.        ],
       [0.        , 1.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 1.        , ..., 0.        , 0.        ,
        0.02230089],
       ...,
       [0.        , 0.        , 0.        , ..., 1.        , 0.        ,
        0.        ],
       [0.01538292, 0.        , 0.        , ..., 0.        , 1.        ,
        0.        ],
       [0.        , 0.        , 0.02230089, ..., 0.        , 0.        ,
        1.        ]])
In [60]:
indices = pd.Series(Netflix.index, index=Netflix['title']).drop_duplicates()
indices
Out[60]:
title
dick johnson is dead        0
blood & water               1
ganglands                   2
jailbirds new orleans       3
kota factory                4
                         ... 
zodiac                   8802
zombie dumb              8803
zombieland               8804
zoom                     8805
zubaan                   8806
Length: 8807, dtype: int64
In [61]:
def get_recommendations(title, cosine_sim=cosine_sim):
    title = title.lower()
    idx = indices[title]
    # print(idx)
    
    # Get the pairwsie similarity scores of all movies with that movie
    sim_scores = list(enumerate(cosine_sim[idx]))
    # print(sim_scores)
    
    # Sort the movies based on the similarity scores
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

    # Get the scores of the 10 most similar movies
    sim_scores = sim_scores[1:11]

    # Get the movie indices
    movie_indices = [i[0] for i in sim_scores]

    # Return the top 10 most similar movies
    return Netflix['title'].iloc[movie_indices]
In [62]:
get_recommendations('Peaky Blinders')
Out[62]:
7683                    our godfather
2646                   my stupid boss
3133                              don
8293                         the fear
7140    jonathan strange & mr norrell
7785                power rangers zeo
8467                       the prison
8539                       the tudors
1510                    the con is on
8391     the legend of michael mishra
Name: title, dtype: object
In [63]:
get_recommendations('Extraction')
Out[63]:
6086              act of valor
1633                   the 2nd
7063                  incoming
2241     the business of drugs
6511    code name: the cleaner
7328                   lockout
1764                   snowden
373         the last mercenary
8686                      waar
4580                   kurbaan
Name: title, dtype: object

Content Based Filtering on Multiple Metrics (Title, Cast, Director, Listed-in, Plot)¶

In [64]:
filledna= Netflix.fillna('')
filledna.head(2)
Out[64]:
show_id type title director cast country date_added release_year rating duration listed_in description
0 s1 Movie dick johnson is dead Kirsten Johnson United States September 25, 2021 2020 PG-13 90 min Documentaries As her father nears the end of his life, filmm...
1 s2 TV Show blood & water Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa September 24, 2021 2021 TV-MA 2 Seasons International TV Shows, TV Dramas, TV Mysteries After crossing paths at a party, a Cape Town t...
In [65]:
def clean_data(x):
        return str.lower(x.replace(" ", ""))
In [66]:
features = ['title', 'director', 'cast', 'listed_in', 'description']
filledna = filledna[features]
filledna.head(2)
Out[66]:
title director cast listed_in description
0 dick johnson is dead Kirsten Johnson Documentaries As her father nears the end of his life, filmm...
1 blood & water Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... International TV Shows, TV Dramas, TV Mysteries After crossing paths at a party, a Cape Town t...
In [67]:
for feature in features:
    filledna[feature] = filledna[feature].apply(clean_data)
    
filledna.head(2)
Out[67]:
title director cast listed_in description
0 dickjohnsonisdead kirstenjohnson documentaries asherfathernearstheendofhislife,filmmakerkirst...
1 blood&water amaqamata,khosingema,gailmabalane,thabangmolab... internationaltvshows,tvdramas,tvmysteries aftercrossingpathsataparty,acapetownteensetsou...
In [68]:
def create_soup(x):
    return x['title']+ ' ' + x['director'] + ' ' + x['cast'] + ' ' +x['listed_in']+' '+ x['description']
In [69]:
filledna['soup'] = filledna.apply(create_soup, axis=1)
filledna.head()
Out[69]:
title director cast listed_in description soup
0 dickjohnsonisdead kirstenjohnson documentaries asherfathernearstheendofhislife,filmmakerkirst... dickjohnsonisdead kirstenjohnson documentarie...
1 blood&water amaqamata,khosingema,gailmabalane,thabangmolab... internationaltvshows,tvdramas,tvmysteries aftercrossingpathsataparty,acapetownteensetsou... blood&water amaqamata,khosingema,gailmabalane...
2 ganglands julienleclercq samibouajila,tracygotoas,samueljouy,nabihaakka... crimetvshows,internationaltvshows,tvaction&adv... toprotecthisfamilyfromapowerfuldruglord,skille... ganglands julienleclercq samibouajila,tracygot...
3 jailbirdsneworleans docuseries,realitytv feuds,flirtationsandtoilettalkgodownamongthein... jailbirdsneworleans docuseries,realitytv feu...
4 kotafactory mayurmore,jitendrakumar,ranjanraj,alamkhan,ahs... internationaltvshows,romantictvshows,tvcomedies inacityofcoachingcentersknowntotrainindia’sfin... kotafactory mayurmore,jitendrakumar,ranjanraj...
In [70]:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity

count_vectorizer = CountVectorizer(stop_words='english')

count_matrix = count_vectorizer.fit_transform(filledna['soup'])

cosine_sim2 = cosine_similarity(count_matrix, count_matrix)
filledna.head(3)
Out[70]:
title director cast listed_in description soup
0 dickjohnsonisdead kirstenjohnson documentaries asherfathernearstheendofhislife,filmmakerkirst... dickjohnsonisdead kirstenjohnson documentarie...
1 blood&water amaqamata,khosingema,gailmabalane,thabangmolab... internationaltvshows,tvdramas,tvmysteries aftercrossingpathsataparty,acapetownteensetsou... blood&water amaqamata,khosingema,gailmabalane...
2 ganglands julienleclercq samibouajila,tracygotoas,samueljouy,nabihaakka... crimetvshows,internationaltvshows,tvaction&adv... toprotecthisfamilyfromapowerfuldruglord,skille... ganglands julienleclercq samibouajila,tracygot...
In [71]:
filledna = filledna.reset_index()
filledna.head(3)
Out[71]:
index title director cast listed_in description soup
0 0 dickjohnsonisdead kirstenjohnson documentaries asherfathernearstheendofhislife,filmmakerkirst... dickjohnsonisdead kirstenjohnson documentarie...
1 1 blood&water amaqamata,khosingema,gailmabalane,thabangmolab... internationaltvshows,tvdramas,tvmysteries aftercrossingpathsataparty,acapetownteensetsou... blood&water amaqamata,khosingema,gailmabalane...
2 2 ganglands julienleclercq samibouajila,tracygotoas,samueljouy,nabihaakka... crimetvshows,internationaltvshows,tvaction&adv... toprotecthisfamilyfromapowerfuldruglord,skille... ganglands julienleclercq samibouajila,tracygot...
In [72]:
indices = pd.Series(filledna.index, index=filledna['title'])
In [73]:
def get_recommendations_new(title, cosine_sim=cosine_sim):
    title=title.replace(' ','').lower()
    idx = indices[title]

    # Get the pairwsie similarity scores of all movies with that movie
    sim_scores = list(enumerate(cosine_sim[idx]))

    # Sort the movies based on the similarity scores
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

    # Get the scores of the 10 most similar movies
    sim_scores = sim_scores[1:11]

    # Get the movie indices
    movie_indices = [i[0] for i in sim_scores]

    # Return the top 10 most similar movies
    return Netflix['title'].iloc[movie_indices]
In [74]:
get_recommendations_new('Peaky Blinders', cosine_sim2)
Out[74]:
3034                    giri / haji
5032    the frankenstein chronicles
8431          the murder detectives
4951                         loaded
4809                  kiss me first
6922                   happy valley
2184                       get even
519                   i am a killer
3789                 killer ratings
4476          terrorism close calls
Name: title, dtype: object
In [75]:
get_recommendations_new('Extraction', cosine_sim2)
Out[75]:
7403               marauders
6759              first kill
8464              the prince
4170                   polar
8268        the darkest dawn
354       the last boy scout
952     the whole nine yards
7985                  set up
1659               hard kill
6088        acts of violence
Name: title, dtype: object

Netflix Platform Growth Over the Years¶

In [78]:
Netflix["date_added"] = pd.to_datetime(Netflix['date_added'])
Netflix['year_added'] = Netflix['date_added'].dt.year

fig, ax = plt.subplots(1, 1, figsize=(18, 10), dpi=100)
color = ["#B20600", "#00092C"]


for i, mtv in enumerate(Netflix['type'].value_counts().index):
    mtv_rel = Netflix[Netflix['type']==mtv]['year_added'].value_counts().sort_index()
    ax.plot(mtv_rel.index, mtv_rel, color=color[i], label=mtv)
    ax.fill_between(mtv_rel.index, 0, mtv_rel, color=color[i], alpha=1)
    
    
# Set Y axis on the right
ax.yaxis.tick_right()

# Set x axis limit & interval
ax.set_xlim(2008,2020)
plt.xticks(np.arange(2008, 2021, 1), fontsize=13)
plt.yticks(fontsize=13)

fig.text(0.28,0.59,"Movies", fontweight="bold", fontfamily='serif', fontsize=40, color='#B20600', alpha=1)
fig.text(0.42,0.59,"v/s", fontweight="bold", fontfamily='serif', fontsize=40, color='#00092C', alpha=1)
fig.text(0.48,0.59,"TV Shows", fontweight="bold", fontfamily='serif', fontsize=40, color='#00092C', alpha=1)

plt.show()

Neflix Distribution Across the Globe¶

In [79]:
country_codes = {'afghanistan': 'AFG',
 'albania': 'ALB',
 'algeria': 'DZA',
 'american samoa': 'ASM',
 'andorra': 'AND',
 'angola': 'AGO',
 'anguilla': 'AIA',
 'antigua and barbuda': 'ATG',
 'argentina': 'ARG',
 'armenia': 'ARM',
 'aruba': 'ABW',
 'australia': 'AUS',
 'austria': 'AUT',
 'azerbaijan': 'AZE',
 'bahamas': 'BHM',
 'bahrain': 'BHR',
 'bangladesh': 'BGD',
 'barbados': 'BRB',
 'belarus': 'BLR',
 'belgium': 'BEL',
 'belize': 'BLZ',
 'benin': 'BEN',
 'bermuda': 'BMU',
 'bhutan': 'BTN',
 'bolivia': 'BOL',
 'bosnia and herzegovina': 'BIH',
 'botswana': 'BWA',
 'brazil': 'BRA',
 'british virgin islands': 'VGB',
 'brunei': 'BRN',
 'bulgaria': 'BGR',
 'burkina faso': 'BFA',
 'burma': 'MMR',
 'burundi': 'BDI',
 'cabo verde': 'CPV',
 'cambodia': 'KHM',
 'cameroon': 'CMR',
 'canada': 'CAN',
 'cayman islands': 'CYM',
 'central african republic': 'CAF',
 'chad': 'TCD',
 'chile': 'CHL',
 'china': 'CHN',
 'colombia': 'COL',
 'comoros': 'COM',
 'congo democratic': 'COD',
 'Congo republic': 'COG',
 'cook islands': 'COK',
 'costa rica': 'CRI',
 "cote d'ivoire": 'CIV',
 'croatia': 'HRV',
 'cuba': 'CUB',
 'curacao': 'CUW',
 'cyprus': 'CYP',
 'czech republic': 'CZE',
 'denmark': 'DNK',
 'djibouti': 'DJI',
 'dominica': 'DMA',
 'dominican republic': 'DOM',
 'ecuador': 'ECU',
 'egypt': 'EGY',
 'el salvador': 'SLV',
 'equatorial guinea': 'GNQ',
 'eritrea': 'ERI',
 'estonia': 'EST',
 'ethiopia': 'ETH',
 'falkland islands': 'FLK',
 'faroe islands': 'FRO',
 'fiji': 'FJI',
 'finland': 'FIN',
 'france': 'FRA',
 'french polynesia': 'PYF',
 'gabon': 'GAB',
 'gambia, the': 'GMB',
 'georgia': 'GEO',
 'germany': 'DEU',
 'ghana': 'GHA',
 'gibraltar': 'GIB',
 'greece': 'GRC',
 'greenland': 'GRL',
 'grenada': 'GRD',
 'guam': 'GUM',
 'guatemala': 'GTM',
 'guernsey': 'GGY',
 'guinea-bissau': 'GNB',
 'guinea': 'GIN',
 'guyana': 'GUY',
 'haiti': 'HTI',
 'honduras': 'HND',
 'hong kong': 'HKG',
 'hungary': 'HUN',
 'iceland': 'ISL',
 'india': 'IND',
 'indonesia': 'IDN',
 'iran': 'IRN',
 'iraq': 'IRQ',
 'ireland': 'IRL',
 'isle of man': 'IMN',
 'israel': 'ISR',
 'italy': 'ITA',
 'jamaica': 'JAM',
 'japan': 'JPN',
 'jersey': 'JEY',
 'jordan': 'JOR',
 'kazakhstan': 'KAZ',
 'kenya': 'KEN',
 'kiribati': 'KIR',
 'north korea': 'PRK',
 'south korea': 'KOR',
 'kosovo': 'KSV',
 'kuwait': 'KWT',
 'kyrgyzstan': 'KGZ',
 'laos': 'LAO',
 'latvia': 'LVA',
 'lebanon': 'LBN',
 'lesotho': 'LSO',
 'liberia': 'LBR',
 'libya': 'LBY',
 'liechtenstein': 'LIE',
 'lithuania': 'LTU',
 'luxembourg': 'LUX',
 'macau': 'MAC',
 'macedonia': 'MKD',
 'madagascar': 'MDG',
 'malawi': 'MWI',
 'malaysia': 'MYS',
 'maldives': 'MDV',
 'mali': 'MLI',
 'malta': 'MLT',
 'marshall islands': 'MHL',
 'mauritania': 'MRT',
 'mauritius': 'MUS',
 'mexico': 'MEX',
 'micronesia': 'FSM',
 'moldova': 'MDA',
 'monaco': 'MCO',
 'mongolia': 'MNG',
 'montenegro': 'MNE',
 'morocco': 'MAR',
 'mozambique': 'MOZ',
 'namibia': 'NAM',
 'nepal': 'NPL',
 'netherlands': 'NLD',
 'new caledonia': 'NCL',
 'new zealand': 'NZL',
 'nicaragua': 'NIC',
 'nigeria': 'NGA',
 'niger': 'NER',
 'niue': 'NIU',
 'northern mariana islands': 'MNP',
 'norway': 'NOR',
 'oman': 'OMN',
 'pakistan': 'PAK',
 'palau': 'PLW',
 'panama': 'PAN',
 'papua new guinea': 'PNG',
 'paraguay': 'PRY',
 'peru': 'PER',
 'philippines': 'PHL',
 'poland': 'POL',
 'portugal': 'PRT',
 'puerto rico': 'PRI',
 'qatar': 'QAT',
 'romania': 'ROU',
 'russia': 'RUS',
 'rwanda': 'RWA',
 'saint kitts and nevis': 'KNA',
 'saint lucia': 'LCA',
 'saint martin': 'MAF',
 'saint pierre and miquelon': 'SPM',
 'saint vincent and the grenadines': 'VCT',
 'samoa': 'WSM',
 'san marino': 'SMR',
 'sao tome and principe': 'STP',
 'saudi arabia': 'SAU',
 'senegal': 'SEN',
 'serbia': 'SRB',
 'seychelles': 'SYC',
 'sierra leone': 'SLE',
 'singapore': 'SGP',
 'sint maarten': 'SXM',
 'slovakia': 'SVK',
 'slovenia': 'SVN',
 'solomon islands': 'SLB',
 'somalia': 'SOM',
 'south africa': 'ZAF',
 'south sudan': 'SSD',
 'spain': 'ESP',
 'sri lanka': 'LKA',
 'sudan': 'SDN',
 'suriname': 'SUR',
 'swaziland': 'SWZ',
 'sweden': 'SWE',
 'switzerland': 'CHE',
 'syria': 'SYR',
 'taiwan': 'TWN',
 'tajikistan': 'TJK',
 'tanzania': 'TZA',
 'thailand': 'THA',
 'timor-leste': 'TLS',
 'togo': 'TGO',
 'tonga': 'TON',
 'trinidad and tobago': 'TTO',
 'tunisia': 'TUN',
 'turkey': 'TUR',
 'turkmenistan': 'TKM',
 'tuvalu': 'TUV',
 'uganda': 'UGA',
 'ukraine': 'UKR',
 'united arab emirates': 'ARE',
 'united kingdom': 'GBR',
 'united states': 'USA',
 'uruguay': 'URY',
 'uzbekistan': 'UZB',
 'vanuatu': 'VUT',
 'venezuela': 'VEN',
 'vietnam': 'VNM',
 'virgin islands': 'VGB',
 'west bank': 'WBG',
 'yemen': 'YEM',
 'zambia': 'ZMB',
 'zimbabwe': 'ZWE'}
In [80]:
import plotly.express as px
from collections import Counter
country_with_code= {}
shows_countries = ", ".join(Netflix['country'].dropna()).split(", ")
for c,v in dict(Counter(shows_countries)).items():
    code = ""
    if c.lower() in country_codes:
        code = country_codes[c.lower()]
    country_with_code[code] = v    

    
fig = px.choropleth(
    list(country_with_code.values()), 
    locations=list(country_with_code.keys()), 
    width=1500, 
    height=700, 
    color_discrete_sequence=px.colors.qualitative.Set1)
fig.show()

Thank you for spending your time on my Netflix EDA¶